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  • Open Access

    ARTICLE

    A Conditionally Anonymous Linkable Ring Signature for Blockchain Privacy Protection

    Quan Zhou1,*, Yulong Zheng1, Minhui Chen2, Kaijun Wei2

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2851-2867, 2023, DOI:10.32604/csse.2023.039908

    Abstract In recent years, the issue of preserving the privacy of parties involved in blockchain transactions has garnered significant attention. To ensure privacy protection for both sides of the transaction, many researchers are using ring signature technology instead of the original signature technology. However, in practice, identifying the signer of an illegal blockchain transaction once it has been placed on the chain necessitates a signature technique that offers conditional anonymity. Some illegals can conduct illegal transactions and evade the law using ring signatures, which offer perfect anonymity. This paper firstly constructs a conditionally anonymous linkable ring signature using the Diffie-Hellman key… More >

  • Open Access

    ARTICLE

    An Adaptive Edge Detection Algorithm for Weed Image Analysis

    Yousef Alhwaiti1,*, Muhammad Hameed Siddiqi1, Irshad Ahmad2

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3011-3031, 2023, DOI:10.32604/csse.2023.042110

    Abstract Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops. Weeds have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural economy. The significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural fields. Some weed methods have been proposed for these fields; however, these algorithms still have challenges as they were implemented against controlled environments. Therefore, in this paper, a… More >

  • Open Access

    ARTICLE

    Optical Based Gradient-Weighted Class Activation Mapping and Transfer Learning Integrated Pneumonia Prediction Model

    Chia-Wei Jan1, Yu-Jhih Chiu1, Kuan-Lin Chen2, Ting-Chun Yao3, Ping-Huan Kuo1,4,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2989-3010, 2023, DOI:10.32604/csse.2023.042078

    Abstract Pneumonia is a common lung disease that is more prone to affect the elderly and those with weaker respiratory systems. However, hospital medical resources are limited, and sometimes the workload of physicians is too high, which can affect their judgment. Therefore, a good medical assistance system is of great significance for improving the quality of medical care. This study proposed an integrated system by combining transfer learning and gradient-weighted class activation mapping (Grad-CAM). Pneumonia is a common lung disease that is generally diagnosed using X-rays. However, in areas with limited medical resources, a shortage of medical personnel may result in… More >

  • Open Access

    ARTICLE

    A Novel Incremental Attribute Reduction Algorithm Based on Intuitionistic Fuzzy Partition Distance

    Pham Viet Anh1,3, Nguyen Ngoc Thuy4, Nguyen Long Giang2, Pham Dinh Khanh5, Nguyen The Thuy1,6,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2971-2988, 2023, DOI:10.32604/csse.2023.042068

    Abstract Attribute reduction, also known as feature selection, for decision information systems is one of the most pivotal issues in machine learning and data mining. Approaches based on the rough set theory and some extensions were proved to be efficient for dealing with the problem of attribute reduction. Unfortunately, the intuitionistic fuzzy sets based methods have not received much interest, while these methods are well-known as a very powerful approach to noisy decision tables, i.e., data tables with the low initial classification accuracy. Therefore, this paper provides a novel incremental attribute reduction method to deal more effectively with noisy decision tables,… More >

  • Open Access

    ARTICLE

    Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

    Ehab Bahaudien Ashary1, Sahar Jambi2, Rehab B. Ashari2, Mahmoud Ragab3,4,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2953-2969, 2023, DOI:10.32604/csse.2023.041523

    Abstract Object Detection is the task of localization and classification of objects in a video or image. In recent times, because of its widespread applications, it has obtained more importance. In the modern world, waste pollution is one significant environmental problem. The prominence of recycling is known very well for both ecological and economic reasons, and the industry needs higher efficiency. Waste object detection utilizing deep learning (DL) involves training a machine-learning method to classify and detect various types of waste in videos or images. This technology is utilized for several purposes recycling and sorting waste, enhancing waste management and reducing… More >

  • Open Access

    ARTICLE

    Hybrid Dynamic Optimization for Multilevel Security System in Disseminating Confidential Information

    Shahina Anwarul1, Sunil Kumar2, Ashok Bhansali3, Hammam Alshazly4,*, Hany S. Hussein5,6

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3145-3163, 2023, DOI:10.32604/csse.2023.041061

    Abstract Security systems are the need of the hour to protect data from unauthorized access. The dissemination of confidential information over the public network requires a high level of security. The security approach such as steganography ensures confidentiality, authentication, integrity, and non-repudiation. Steganography helps in hiding the secret data inside the cover media so that the attacker can be confused during the transmission process of secret data between sender and receiver. Therefore, we present an efficient hybrid security model that provides multifold security assurance. To this end, a rectified Advanced Encryption Standard (AES) algorithm is proposed to overcome the problems existing… More >

  • Open Access

    ARTICLE

    Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

    Sarah M. Alhammad1, Doaa Sami Khafaga1,*, Aya Y. Hamed2, Osama El-Koumy3, Ehab R. Mohamed3, Khalid M. Hosny3

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2939-2952, 2023, DOI:10.32604/csse.2023.041011

    Abstract Face mask detection has several applications, including real-time surveillance, biometrics, etc. Identifying face masks is also helpful for crowd control and ensuring people wear them publicly. With monitoring personnel, it is impossible to ensure that people wear face masks; automated systems are a much superior option for face mask detection and monitoring. This paper introduces a simple and efficient approach for masked face detection. The architecture of the proposed approach is very straightforward; it combines deep learning and local binary patterns to extract features and classify them as masked or unmasked. The proposed system requires hardware with minimal power consumption… More >

  • Open Access

    ARTICLE

    Security Test Case Prioritization through Ant Colony Optimization Algorithm

    Abdulaziz Attaallah1, Khalil al-Sulbi2, Areej Alasiry3, Mehrez Marzougui3, Mohd Waris Khan4,*, Mohd Faizan4, Alka Agrawal5, Dhirendra Pandey5

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3165-3195, 2023, DOI:10.32604/csse.2023.040259

    Abstract Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems. One of the challenges in software security testing is test case prioritization, which aims to reduce redundancy in fault occurrences when executing test suites. By effectively applying test case prioritization, both the time and cost required for developing secure software can be reduced. This paper proposes a test case prioritization technique based on the Ant Colony Optimization (ACO) algorithm, a metaheuristic approach. The performance of the ACO-based technique is evaluated using the Average Percentage of Fault… More >

  • Open Access

    ARTICLE

    Enhanced 3D Point Cloud Reconstruction for Light Field Microscopy Using U-Net-Based Convolutional Neural Networks

    Shariar Md Imtiaz1, Ki-Chul Kwon1, F. M. Fahmid Hossain1, Md. Biddut Hossain1, Rupali Kiran Shinde1, Sang-Keun Gil2, Nam Kim1,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2921-2937, 2023, DOI:10.32604/csse.2023.040205

    Abstract This article describes a novel approach for enhancing the three-dimensional (3D) point cloud reconstruction for light field microscopy (LFM) using U-net architecture-based fully convolutional neural network (CNN). Since the directional view of the LFM is limited, noise and artifacts make it difficult to reconstruct the exact shape of 3D point clouds. The existing methods suffer from these problems due to the self-occlusion of the model. This manuscript proposes a deep fusion learning (DL) method that combines a 3D CNN with a U-Net-based model as a feature extractor. The sub-aperture images obtained from the light field microscopy are aligned to form… More >

  • Open Access

    ARTICLE

    CeTrivium: A Stream Cipher Based on Cellular Automata for Securing Real-Time Multimedia Transmission

    Osama S. Younes1,2,*, Abdulmohsen Alharbi1, Ali Yasseen1, Faisal Alshareef1, Faisal Albalawi1, Umar A. Albalawi1,3

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2895-2920, 2023, DOI:10.32604/csse.2023.040162

    Abstract Due to their significant correlation and redundancy, conventional block cipher cryptosystems are not efficient in encrypting multimedia data. Stream ciphers based on Cellular Automata (CA) can provide a more effective solution. The CA have recently gained recognition as a robust cryptographic primitive, being used as pseudorandom number generators in hash functions, block ciphers and stream ciphers. CA have the ability to perform parallel transformations, resulting in high throughput performance. Additionally, they exhibit a natural tendency to resist fault attacks. Few stream cipher schemes based on CA have been proposed in the literature. Though, their encryption/decryption throughput is relatively low, which… More >

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